ICG: a wiki-driven knowledgebase of internal control genes for RT-qPCR normalization

Abstract Real-time quantitative PCR (RT-qPCR) has become a widely used method for accurate expression profiling of targeted mRNA and ncRNA. Selection of appropriate internal control genes for RT-qPCR normalization is an elementary prerequisite for reliable expression measurement. Here, we present ICG (http://icg.big.ac.cn), a wiki-driven knowledgebase for community curation of experimentally validated internal control genes as well as their associated experimental conditions. Unlike extant related databases that focus on qPCR primers in model organisms (mainly human and mouse), ICG features harnessing collective intelligence in community integration of internal control genes for a variety of species. Specifically, it integrates a comprehensive collection of more than 750 internal control genes for 73 animals, 115 plants, 12 fungi and 9 bacteria, and incorporates detailed information on recommended application scenarios corresponding to specific experimental conditions, which, collectively, are of great help for researchers to adopt appropriate internal control genes for their own experiments. Taken together, ICG serves as a publicly editable and open-content encyclopaedia of internal control genes and accordingly bears broad utility for reliable RT-qPCR normalization and gene expression characterization in both model and non-model organisms.

[1]  Jaco Vangronsveld,et al.  Reliable Gene Expression Analysis by Reverse Transcription-Quantitative PCR: Reporting and Minimizing the Uncertainty in Data Accuracy[W][OPEN] , 2014, Plant Cell.

[2]  V. Beneš,et al.  The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. , 2009, Clinical chemistry.

[3]  S A Bustin,et al.  Quantitative real-time RT-PCR--a perspective. , 2005, Journal of molecular endocrinology.

[4]  Jacques Rougemont,et al.  GETPrime: a gene- or transcript-specific primer database for quantitative real-time PCR , 2011, Database J. Biol. Databases Curation.

[5]  F. Speleman,et al.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes , 2002, Genome Biology.

[6]  M. Pfaffl,et al.  Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper – Excel-based tool using pair-wise correlations , 2004, Biotechnology Letters.

[7]  W. Scheible,et al.  Eleven Golden Rules of Quantitative RT-PCR , 2008, The Plant Cell Online.

[8]  SH Song,et al.  The BIG Data Center: from deposition to integration to translation , 2016, Nucleic Acids Res..

[9]  Wenwu Cui,et al.  qPrimerDepot: a primer database for quantitative real time PCR , 2006, Nucleic Acids Res..

[10]  Claus Lindbjerg Andersen,et al.  Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets , 2004, Cancer Research.

[11]  Winston A Hide,et al.  Big data: The future of biocuration , 2008, Nature.

[12]  B. Tilton,et al.  Housekeeping gene variability in normal and cancerous colorectal, pancreatic, esophageal, gastric and hepatic tissues. , 2005, Molecular and cellular probes.

[13]  F. Zhao,et al.  CIRI: an efficient and unbiased algorithm for de novo circular RNA identification , 2015, Genome Biology.

[14]  Frank Speleman,et al.  A novel and universal method for microRNA RT-qPCR data normalization , 2009, Genome Biology.

[15]  Thomas D. Schmittgen,et al.  Analyzing real-time PCR data by the comparative CT method , 2008, Nature Protocols.

[16]  S. Davis Faculty Opinions recommendation of Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. , 2006 .

[17]  Jaime Rodriguez-Canales,et al.  Quantitative RT-PCR gene expression analysis of laser microdissected tissue samples , 2009, Nature Protocols.

[18]  Xiaojiao Han,et al.  Selection and Validation of Reference Genes for Real-Time Quantitative PCR in Hyperaccumulating Ecotype of Sedum alfredii under Different Heavy Metals Stresses , 2013, PloS one.

[19]  Xiaowei Wang,et al.  PrimerBank: a resource of human and mouse PCR primer pairs for gene expression detection and quantification , 2009, Nucleic Acids Res..

[20]  M. Pfaffl,et al.  A new mathematical model for relative quantification in real-time RT-PCR. , 2001, Nucleic acids research.